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The identification of threshold function (TF), which determines whether a Boolean function can be represented by an linear threshold logic gate (LTG) or not, is a fundamental but important task in the theories of threshold logic. In this paper, we propose a more efficient and effective algorithm of TF identification by constructing the system of irredundant inequalities and adjusting the weight assignment comprehensively. This is the first non-ILP-based approach that is able to identify all the eight-input TFs. The experimental results demonstrated that the proposed approach is more effective than all the existing non-ILP-based approaches and the LTGs obtained by the proposed approach are optimal for near 100% cases. For TFs with 9–15 inputs, the proposed approach can identify 100 000 randomly generated TFs as well in a reasonable CPU time. [ABSTRACT FROM AUTHOR]

Memristor, which is a two-terminal nanodevice, widely used in various fields, e.g., machine learning and neuromorphic systems, has attracted much attention these years. Memristor can also be used to realize an implication logic gate and thus logic circuits. However, the fanouts in a memristor-based logic circuit have some constraints and need to be processed with special care. On the other hand, in addition to the number of memristors, the number of operational pulses is another metric to measure the quality of a memristor-based logic circuit. Hence, in this paper, we propose a synthesis algorithm to deal with the fanout problems in memristor-based logic circuits using implication logic gates for having a minimal number of operational pulses. We conducted experiments on a set of MCNC benchmarks. The experimental results show that the proposed algorithm can reduce 29% operational pulses and 36% memristor count on average compared with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Single-electron transistor (SET) at room temperature has been demonstrated as a promising device for extending Moore’s law due to its ultralow-power consumption. Previous works proposed mapping approaches to implement Boolean functions on SET arrays. However, these approaches were based on an ideal assumption that the SET arrays are defect-free. Recently, a diagnosis method was proposed targeting at defective SET arrays. However, the approach was static, such that the performance is inefficient. As a result, in this paper, we propose a dynamic diagnosis approach that can efficiently identify the locations and the types of the defects in the SET arrays. The experimental results show that the proposed dynamic diagnosis approach can achieve the same results as the previous work with much less CPU time on a set of benchmarks. Furthermore, the proposed method spent a few seconds while the previous work exceeded the CPU time limit of 3600 s on some benchmarks. [ABSTRACT FROM PUBLISHER]

T cells with CD4–CD8– (double negative, DN) phenotype in MRL-lpr/lpr mouse serve as a model to establish the correlation between the extremely low IL-2 gene expression and the specific signaling inactivation. The extent of nonresponsiveness in lpr DN cells was distinctive in several unusual defects. First, the poor IL-2 production in lpr DN cells could not be restored by supplement of signals known to augment IL-2 response in normal T cells. Second, the activations of both mitogen-activated protein (MAP) kinase and c-Jun N-terminal kinase (JNK) were attenuated in lpr DN cells upon direct activation by TPA/A23187. Third, IL-2 mRNA was degraded much faster in lpr DN cells than that in normal T cells. Fourth, of the four major transcriptional elements on IL-2 promoter, only AP-1 and nuclear factor of activated T cells (NFAT)-binding activities were suppressed in lpr DN T cells. Altogether, these results suggest that an extremely low level of IL-2 production in lpr DN T cells was due to both the increased instability of mRNA and the reduced activation of IL-2 gene promoter, the latter defect could be attributed to the inactivation of AP-1 and NF-AT as well as the poor activation of the upstream MAP kinase and JNK. [ABSTRACT FROM AUTHOR]

The basic oxygen furnace slag is a major waste by-product generated from steel-producing plants. It possesses excellent characteristics and can be used as a natural aggregate. Chemically, the basic oxygen furnace slag encloses free CaO and free MgO, which is the main reason for the expansion crisis since these free oxides of alkaline earth metals react with water to form their hydroxide yields. The objective of the present research study is to stabilize the basic oxygen furnace slag by using innovative geopolymer technology, as their matrix contains a vast quantity of free silicon, which can react with free CaO and free MgO to form stable silicate compounds resulting in the prevention of the basic oxygen furnace slag expansion predicament. Lab-scale and ready-mixed plant pilot-scale experimental findings revealed that the compressive strength of fine basic oxygen furnace slag-based geopolymer mortar can achieve a compressive strength of 30–40 MPa after 28 days, and increased compressive strength, as well as the expansion, can be controlled less than 0.5% after ASTM C151 autoclave testing. Several pilot-scale cubic meters basic oxygen furnace slag-based geopolymer concrete blocks were developed in a ready-mixed plant. The compressive strength and autoclave expansion test results demonstrated that geopolymer technology does not merely stabilize the basic oxygen furnace slag production issue totally, but also turns the slags into value-added products. [ABSTRACT FROM AUTHOR]

ZnO nanowall networks with a honeycomblike pattern on GaN/sapphire substrates were deposited by metalorganic chemical vapor deposition (MOCVD) without using any metal catalysts. The effects of growth temperature and VI/II ratio on the surface morphology and optical properties of ZnO nanowall networks were investigated by scanning electron microscopy (SEM) and photoluminescence (PL) analysis. The SEM image obtained shows a prominent nanowall-network structure when the growth temperature is higher than 550 °C. The wall width and network size of ZnO nanowall-network structures grown by MOCVD were found to change depending on DEZn flow rate. The surface morphology of ZnO structures was observed at different time intervals from 10 to 40 min to investigate the formation mechanism of ZnO nanowall networks. The room-temperature PL measurement of ZnO nanostructures grown on GaN/sapphire substrates shows high-intensity ultraviolet peaks at 385 nm without any “green peak”. The PL spectrum suggests that the quantum confinement effects are caused by the nanostructure of ZnO. [ABSTRACT FROM AUTHOR]